Fault isolation in electronic devices such as network elements and communication switches is a challenging task due to enormity of alarm data, ambiguous alarm information (false or missing alarms, redundant alarms, out of sequence alarms etc.) and insufficient alarm-based information for some faults. Hence, there is a need for a mechanism for fast and efficient processing of a large number of alarms.
Existing solutions for fault isolation are mainly of the following categories based on the approach employed for fault diagnosis and isolation:                Rule Based Event Correlation        
Rule based systems are based on expert knowledge to identify set of rules to correlate alarms to faults. In case of large network systems, these rules can be very complex and large in number resulting in difficulties in the management of these rules and leading to inaccurate inference of faults. These systems are sensitive to missing and false alarms and out of sequence alarms.                Model Based Reasoning        
In model based systems, fault isolation is based on the models of the systems. The alarms are processed with the help of these models to isolate the faults. However, it is difficult to model complex systems to achieve accurate fault isolation.                Case Based Reasoning        
Case based reasoning is based on the history of solved problems. A strategy to solve the problem is based on the case history. These systems require exhaustive case database consisting of solution strategy for each case. Huge case database result in high search time and hence affecting the time required for fault isolation.                Probability Network/Bayesian Network        
Fault isolation based on Bayesian network is used in conjunction with other fault isolation mechanisms. Probability networks may be advantageous if they can produce hypotheses with a precise confidence level.